In Phase I of the "Decision Support System to Identify the At-Risk Fetus" a prototype Intelligent Decision Support System (IDSS) was developed to interpret electronic fetal monitoring (EFM) data. In Phase II, the researchers propose to complete development and test the IDSS in preparation for full clinical trials and commercialization. The research is based on the hypothesis that intrapartum EFM and the resulting FHR and UC data provide information that can assist physicians in more accurately differentiating between healthy and at-risk fetuses. The system uses morphological filters to process signals at different scales, a neural network to better recognize FHR and UC patterns despite EFM noise, and a fuzzy relational structure outcome inferencing. In Phase I evidence was generated supporting the hypothesis when the IDSS with little training on only 50 cases differentiated between healthy and at-risk fetuses as well, and perhaps slightly better than three board certified obstetricians with over 40 years of combined clinical experience. The proposed Phase II improvements to IDSS would increase the system's sensitivity and specificity so as to assist clinicians in more accurately identifying at-risk fetuses, as well as more confidently identifying healthy fetuses so as to avoid unnecessary interventions. PROPOSED COMMERCIAL APPLICATIONS The successful completion of Phase II should lead to a system which can be used in hospitals and clinicians' offices to better identify the at-risk fetus, thereby allowing for the timely implementation of efficacious interventions and a reduction in the use of invasive obstetrical interventions, including cesarean deliveries, without adversely impacting outcomes. The use of this system in hospitals and private clinicians' offices will also help reduce the costs of national health care.